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Have the conversation: Coaching drivers to safety with data

Every fleet wants to increase safety and mitigate risk — from speeding to distracted driving to roll stability. But risk that you cannot see is risk that you cannot manage. With effective driver coaching and the right use of data, fleets can help prevent incidents and accidents — and increase safety.

Aug 27, 2018

Every fleet wants to increase safety and mitigate risk — from speeding to distracted driving to roll stability. But risk that you cannot see is risk that you cannot manage. With effective driver coaching and the right use of data, fleets can help prevent incidents and accidents — and increase safety.

Mitigating risk with technology and coaching

Fleet managers who continuously monitor risk will help prevent more safety incidents and accidents from occurring. Implementing safety policies and programs is a good start, but the right technology and proper training and coaching methods further increase safety.

Utilizing technology and coaching can reduce the possibility of behavior leading to an incident or accident. It’s easy to understand how technologies such as lane departure warning systems, stability controls, and speed monitoring systems help increase safety; they prevent habits such as swerving, weaving, and speeding from taking place. Then, there are technologies like navigation that promote safety for other reasons. After all, a lost driver is a frustrated driver, which could be a dangerous driver.

Critical event reporting is another tool that can help fleets improve safety. It gives fleet managers visibility to see risk before it becomes a bigger issue. Onboard sensors in critical event reporting let managers know when events such as hard braking, follow time violations, and forward collision occur. Some in-cab video solutions are fully integrated with critical event reporting. Managers can request access to a video stored in the camera directly from the software.

In-cab video can be used in driver training and coaching, often making difficult conversations much easier to have. When a safety manager can produce the video of a driver with poor following distance leading to a panic stop, the video speaks for itself. The manager won’t have to say much except to ensure the driver is okay, review documentation, and reinforce following distance training.

On the other hand, managers can also see all the things their drivers are doing right. In those instances, the video will show that the driver did everything he or she could to avoid an accident. Inward-facing cameras can further exonerate drivers because they can prove, for instance, that a driver wasn’t on his or her cell phone when an incident occurred.

It should be noted that many of these safety technologies create large amounts of data. Add this safety system data to data from vehicle engines, GPS, and Hours of Service and the amount available to fleets is almost inconceivable. But data is where managers get the visibility they need to mitigate risk and make better decisions about the fleet. The more technology vendors and engine manufacturers integrate, the more connected and easier to understand that data becomes. When tightly integrated, telematics devices can also take that data and create onboard and back-office notifications, for example if four lane departures happen in one minute or if an adaptive cruise system engages the brakes.

Identifying and helping at-risk drivers with predictive analytics

With a predictive model, fleets can identify individuals that are at risk for a preventable accident. Predictive models utilize data such as prior employment, trips, loaded miles, and empty miles to predict risk. The Omnitracs Analytics team builds custom models based on a fleet’s own data set to identify data patterns and data pattern changes that are indicative of individuals that are going to have an accident in the near future.

It is important to note that by doing this, we are not identifying bad drivers, nor are they identified due to some problem with the driver manager. These are drivers that are identified as at-risk based on statistical analysis. Good drivers can still have accidents or incidents.

Once the predictive model identifies at-risk drivers, relational coaching needs to occur. This coaching can happen through transactional conversations, which are what managers have when something like a hard brake occurs and they need to discuss or have remedial training with a driver.

Managers can also take the relational conversation approach, which is when they speak with their drivers about stress sources. Drivers dealing with issues related to health, family, finances, pay, hours, or work conditions might be flagged as at risk for an accident. When that happens, managers need to have a proactive and positive conversation to uncover underlying issues and find ways to help.

According to a 2017 study in Transportation Journal, driver stress goes beyond health, family, finance, and work conditions. Other driver stressors include feelings of isolation from being away from home and lack of respect from colleagues or customers.

While it’s very important for drivers and driver managers to have a good relationship, many companies opt to have human resources make the phone call. These are not comfortable or easy conversations to have, but they are important and can be a growing exercise for a manager. The designated people must be trained on how to properly hold these important conversations.

Because these conversations add to a driver manager or safety manager’s duties, it is recommended to concentrate only on a small segment of your population. When building predictive models for carriers, the Omnitracs Analytics team only identifies individuals that are at risk or look like they’ll have an accident in the near future.

When good drivers are at risk

A real-life example of a driver that was flagged as at-risk is about a good driver. His manager thought very highly of him and he had a long and safe driving record. But over time, his score deteriorated to the point where he was identified as someone who needed a proactive intervention. Unfortunately, the manager did not heed the prediction and the driver ultimately had an accident. When the manager dug into why the accident happened, he saw that the driver had been leaving his house later, which was one of the predictors associated with this fleet’s model. The driver also had speeding events.

Since the driver historically had never driven aggressively, the manager interviewed him only to find out that the driver’s house had recently been damaged in a storm and he was trying to repair the damage on his own. So, he was trying to work but get home in a timely fashion to work on his house. As a result, he drove more aggressively and left the house later.

Speaking with a driver one time is not sufficient — there needs to be a continuous improvement cycle. An action plan should be determined based on the initial conversation and it’s the responsibility of the manager to follow up with that driver. Even if the manager took an action to assist the driver or eliminate the stress, there are circumstances where that stressor is still in their lives and that data pattern and data pattern change will still exist. Maintaining relationships and a feedback loop with drivers is very important.

Senior level endorsement is critical to the success of a program like this. This relational program is very different from the transactional one described earlier but fleet managers should have both types of conversations — at separate times — with their drivers.

Adding advanced safety technologies and both types of conversations to safety programs can help fleets significantly mitigate risk.